目的:多发性硬化(MS)与髓鞘少突胶质细胞糖蛋白免疫球蛋白G(MOG-IgG)相关疾病(MOGAD)之间存在临床重叠,难以区分两种疾病。这里,我们测量并比较了容易获得的中性粒细胞与淋巴细胞比率(NLR),血小板与淋巴细胞比率(PLR),和单核细胞与淋巴细胞比率(MLR)来确定这三种生物标志物是否可以帮助区分疾病发作时的MOGAD和MS。这三种生物标志物对MOGAD和MS复发的影响也需要探索。
方法:在本回顾性分析中,我们获得了MOGAD(N=31)和MS(N=50)首次发作的临床和临床数据.电子病历用于收集人口统计数据(性别,发病年龄),临床症状,EDSS开始时,和药物治疗。主要结果是发病后一年内复发。记录四个血液学参数,包括中性粒细胞计数,血小板计数,淋巴细胞计数,和单核细胞计数。NLR,PLR,计算和MLR,并在MOGAD之间进行比较,MS,HC。进行受试者操作曲线(ROC)分析以评估NLR的能力,PLR,和MLR来区分MOGAD和MS,MOGAD和HC,分别。进行逻辑回归分析以确定NLR/PLR/MLR对发病一年内MOGAD/MS复发的影响。
结果:与HC相比,NLR在MOGAD和MS中显著较高(分别为p<0.001,p=0.04)。与HC相比,MOGAD中的PLR和MLR升高(分别为p<0.001,p<0.001),MS和MLR也在统计学上高于HC(p=0.023)。值得注意的是,与MS相比,MOGAD中的NLR和PLR要高得多(分别为p<0.001,p=0.001),但是在MOGAD和MS之间没有发现关于MLR的显着差异。基于ROC曲线分析,我们发现使用NLR,PLR,和MLR区分MOGAD和MS产生的ROC曲线下面积(AUC)值分别为0.794、0.727和0.681。同时,NLR的AUC,PLR,区分MOGAD和HC的MLR分别为0.926、0.772和0.786。此外,后勤分析显示PLR与MOGAD复发之间存在显著正相关.
结论:NLR有助于在疾病发作中区分MOGAD和MS,PLR升高与MOGAD复发有关。
OBJECTIVE: Clinical overlap is observed between multiple sclerosis (MS) and myelin oligodendrocyte glycoprotein immunoglobulin-G (MOG-IgG) associated disease (MOGAD) and the difficulty in distinguishing between the two diseases. Here, we measured and compared the readily available neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and monocyte to lymphocyte ratio (MLR) to determine whether these three biomarkers can help to distinguish MOGAD and MS at disease onset. The impact of these three biomarkers on MOGAD and MS relapse also needs to be explored.
METHODS: In this retrospective analysis, we obtained clinical and paraclinical data from the first attacks of MOGAD (N = 31) and MS (N = 50). Electronic medical records were used to collect demographic data (gender, age at onset), clinical symptoms, EDSS at onset, and medical treatments. The primary outcome was relapse within one year of onset. Four hematological parameters were recorded, including neutrophil count, platelet count, lymphocyte count, and monocyte count. NLR, PLR, and MLR were calculated and compared between MOGAD, MS, and HC. Receiver operator curve (ROC) analysis was performed to assess the ability of NLR, PLR, and MLR to distinguish between MOGAD and MS, MOGAD and HC, respectively. A logistic regression analysis was performed to determine the impact of NLR/PLR/MLR on MOGAD/MS relapse within one year of onset.
RESULTS: Compared to HC, NLR is significantly higher in MOGAD and MS (p<0.001, p = 0.04, respectively). The PLR and MLR are elevated in MOGAD compared to HC (p<0.001, p<0.001, respectively), and MLR in MS are also statistically higher than in HC (p = 0.023). It is worth noting that NLR and PLR were much higher in MOGAD compared to MS (p<0.001, p = 0.001, respectively), but a significant difference regarding MLR has not been found between MOGAD and MS. Based on ROC curve analyses, we found that using NLR, PLR, and MLR to discriminate between MOGAD and MS yielded a ROC-plot area under the curve (AUC) value of 0.794, 0.727, and 0.681, respectively. Meanwhile, the AUC of NLR, PLR, and MLR to discriminate between MOGAD and HC were 0.926, 0.772, and 0.786. Furthermore, the logistics analysis revealed a significant positive association between PLR and MOGAD relapse.
CONCLUSIONS: NLR helps differentiate MOGAD and MS in disease onset, and higher PLR was related to MOGAD relapse.